Filter Results
170811 results
The raw sequencing data obtained from hamsters treated with different interventions including 1) standard diet (control); (2) standard diet and monosodium glutamate (MSG) in drinking water (MSG); (3) high-fat and high-fructose diets (HFF), and (4) MSG+HFF.
Data Types:
  • Dataset
  • File Set
The PICO Statements dataset is a collection of 130 abstracts from Randomized Clinical Trials and Controlled Trials, manually annotated by medical practitioners, to identify sentences that not only contain all four PICO elements but also answer clinically stated questions. These sentences are referred to as PICO Statements. In Evidence-Based Medicine (EBM), the PICO framework is used by medical practitioners to narrow the search space and enable faster decision-making towards treatment procedures. The framework is named after the four elements that comprise it, Population, Intervention, Comparator and Outcome. Previous datasets focus on identifying either whole sentence to a single PICO element or, more recently, the sequence of tokens in the sentence that describe each element. Similar to previous research, we consider Intervention and Comparator as one element in our annotation scheme. For each sentence, we binary annotate the existence of each PICO element individually and if the sentence is a PICO Statement. The dataset is offered, in an abstract per file manner, in two formats: 1) XML format, for sentence classification. The XML format present each abstract, along with its title, annotated on a sentence level, with all four annotations present for each sentence in a binary format. The XML Schema (.xsd) files are also available in the miscellaneous folder. 2) pseudo-IOB format, for PICO entity prediction. The pseudo-IOB format, presents each abstract, along with its title, annotated on a token level, with the same binary annotations repeating for each token in the sentence. The binary annotations in the pseudo-IOB format are corresponding to the PICO elements in the following order: Population, Intervention/Comparator, Outcome, PICO Statement. In both annotation schemes contain the same abstracts and the file names are corresponding to the PubMedIDs of the publications from which the abstracts originate.
Data Types:
  • Dataset
  • File Set
Geometric and energetic features of halogenated rotamers of the following backbone structures, C-C, N-N, P-P, O-O, S-S, N-P, O-S, C-N, C-P, C-O, C-S, N-O, N-S, P-O and P-S from quantum chemical calculations are presented. The data set is considered to be comprehensive combinations of non-metal elements in the form abcx-ydef whereby a,b,c,d,e,f are halogen (fluorine to iodine), hydrogen or a lone pair and x,y are carbon, nitrogen, phosphorus, oxygen and sulfur. Preliminary work on all possible halogenation of methane, ammonia, phosphine, water and hydrogen sulfide are also included.
Data Types:
  • Dataset
  • File Set
This dataset contains cross-sectional and longitudinal tongue images and assessments obtained from 206 ALS patients and 104 age- and sex-matched controls that underwent high-resolution ultrasound (HRUS) and 3T MRI. In each image, the tongue is delineated in an additional region of interest (ROI) file provided for each of the coronal cross-sections acquired via HRUS and the midsagittal slices from the sagittal cerebral 3D-MPRAGE 3T MRI of the head. For these ROIs size and mean intensity markers are calculated. From the MRI images quantitative parameters for the shape and relative position of the tongue are derived. For each individual in the dataset these obtained markers are provided along with demographic and disease specific information in accompanying lists. The dataset can be combined with other data to increase statistical power or to extend the analysis with more advanced algorithms to implement and study additional markers for size, shape or texture of the tongue in ALS patients and controls.
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
  • File Set
Data accompanied with the paper "Reliability and Validity of the Turkish Version of the Health Professionals Communication Skills Scale (HP-CSS)". The sample consisted of 394 health professionals in Turkey.
Data Types:
  • Dataset
  • File Set
resources from the w.p. 'Uncertainty and stochastic theories on derivatives and risk valuation', by C. Alexander Grajales, Santiago Medina, 2020 * Matlab code * output data * paper figures
Data Types:
  • Dataset
  • File Set
This dataset is about a systematic review of unsupervised learning techniques for software defect prediction (our related paper: "A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction" in Information and Software Technology [accepted in Feb, 2020] ). We conducted this systematic literature review that identified 49 studies which satisfied our inclusion criteria containing 2456 individual experimental results. In order to compare prediction performance across these studies in a consistent way, we recomputed the confusion matrices and employed MCC as our main performance measure. From each paper we extracted: Title, Year, Journal/conference, 'Predatory' publisher? (Y | N), Count of results reported in paper, Count of inconsistent results reported in paper, Parameter tuning in SDP? (Yes | Default | ?) and SDP references(SDPRefs OrigResults | SDPRefs |SDPNoRefs | OnlyUnSDP). Then from within each paper, we extracted for each experimental result including: Prediction method name (e.g., DTJ48), Project name trained on (e.g., PC4), Project name tested on (e.g., PC4), Prediction type (within-project | cross-project), No. of input metrics (count | NA), Dataset family (e.g., NASA), Dateset fault rate (%), Was cross validation used? (Y | N | ?), Was error checking possible? (Y | N), Inconsistent results? (Y | N | ?), Error reason description (text), Learning type (Supervised | Unsupervised), Clustering method? (Y | N | NA), Machine learning family (e.g., Un-NN), Machine learning technique (e.g., KM), Prediction results (including TP, TN, FP, FN, etc.).
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
  • Document
  • Text
  • File Set
Results of EMD-based Nonstationary Frequency Analysis over South Korea with Climate Indices for different lags
Data Types:
  • Dataset
  • File Set
Associated research in : Gordon, B. L., Paige, G. B., Miller, S. N., Claes, N., & Parsekian, A. D. (2020). Field scale quantification indicates potential for variability in return flows from flood irrigation in the high altitude western US. Agricultural Water Management, 232, 106062. Readme: The included files are: Calculated Flow, Calculated_Losses, Calculated_Return_Flows, ET_Not_Interpolated, Precipitation, and GIS Database. All the data (except GIS) are in tab delimited ASCII files. GIS data are in standard formats, most site specific information including soils, meadow delineation, instrumentation, etc. can be found in the site_information file. Flow data (Calculated_Flow, Calculated_Losses, Calculated_Return_Flows) were obtained using developed rating curves at each site, where each stilling well was instrumented with a pressure transducer (Level TROLL 500 Data Logger, In-Situ, USA) and manual flow measurements consisting of 25+ individual points for each measurement were made using an electromagnetic current meter (MF Pro, OTT Hydromet, USA). ET data include both measurements from a Large Aperture Scintillometer (LAS MKII, Kipp & Zonen, NLD) and from Penman-Monteith Calculations performed on raw meteorological data collected on site. For Penman-Monteith, we include both raw values and values modified using a crop coefficient from Pochop et al. (1992). Precipitation data were collected using a tipping bucket rain gauge (Rain Collector II, Davis Instruments, USA). All data (except the ET data for the scintillometer) are from May 2015 to October 2015; the ET data from the scintillometer are from June 2015 to October 2015. If you have any questions, or would like raw flow data or unprocessed meterological data, please contact me via email at: beatrice.gordon1@gmail.com
Data Types:
  • Other
  • Dataset
  • Text
  • File Set
Data and code for: Time-Varying Causality between Bond and Oil Markets of the United States: Evidence from Over One and Half Centuries of Data
Data Types:
  • Dataset
  • File Set
3